Jocelyn CHANUSSOT
Professeur Grenoble-INP
Equipe SIGnal iMAge PHYsique
Département Images et Signal
ME CONTACTER / CONTACT ME
Mail : jocelyn.chanussot@gipsa-lab.grenoble-inp.fr

11 rue des mathématiques
Domaine Universitaire
BP 46
38402 Saint Martin d'Hères cedex

Bureau D1136
Tél.33 (0)4 76 82 62 73
Fax : 33 (0)4 76 57 47 90
PUBLICATIONS RECENTES / RECENT PUBLICATIONS
Les derniéres publications de la collection Gipsa dans HAL

HYEP - Hyperspectral Imagery for Environmental Urban PlanningProgramme : ANR Systèmes urbains durables Édi9on : 2014

Christiane Weber, Xavier Briottet, Clément Mallet, Sébastien Gadal, Yannick Devile, et al.. HYEP - Hyperspectral Imagery for Environmental Urban Planning. Journées ADEME / ANR - La recherche au service de la transition énergétique, Jun 2018, d’Issy-les-Moulineaux, France. 2018. 〈hal-01875894〉

Endmembers as Directional Data for Robust Material Variability Retrieval in Hyperspectral Image Unmixing

Lucas Drumetz, Jocelyn Chanussot, A Iwasaki. Endmembers as Directional Data for Robust Material Variability Retrieval in Hyperspectral Image Unmixing. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Apr 2018, Calgary, Alberta, Canada. ICASSP 2018 - Proceedings, 2018, 〈https://2018.ieeeicassp.org/〉. 〈hal-01800076〉

An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data

Théo Masson, Marie Dumont, Mauro Mura, Pascal Sirguey, Simon Gascoin, et al.. An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data. Remote Sensing, MDPI, 2018, 10 (4), 〈10.3390/rs10040619〉. 〈hal-01888531〉

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot, et al.. Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation. Submitted for Journal (Version 2). 2018. 〈hal-01687733〉

4DCAF: A temporal approach for denoising hyperspectral image sequences

Blanca Priego, Richard Duro, Jocelyn Chanussot. 4DCAF: A temporal approach for denoising hyperspectral image sequences. Pattern Recognition, Elsevier, 2017, 72, pp.433 - 445. 〈10.1016/j.patcog.2017.07.023〉. 〈hal-01687059〉

Evaluation of the New Information in the H/α Feature Space Provided by ICA in PolSAR Data Analysis

Leandro Pralon, Gabriel Vasile, Mauro Dalla Mura, Jocelyn Chanussot. Evaluation of the New Information in the H/α Feature Space Provided by ICA in PolSAR Data Analysis. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (12), pp.6893-6909. 〈10.1109/TGRS.2017.2735992〉. 〈hal-01593483〉

Multimorphological Superpixel Model for Hyperspectral Image Classification

Tianzhu Liu, Yanfeng Gu, Jocelyn Chanussot, Mauro Dalla Mura. Multimorphological Superpixel Model for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (12), pp.6950 - 6963. 〈10.1109/TGRS.2017.2737037〉. 〈hal-01665290〉

Multiple Kernel Learning for Hyperspectral Image Classification: A Review

Yanfeng Gu, Jocelyn Chanussot, Xiuping Jia, Jon Atli Benediktsson. Multiple Kernel Learning for Hyperspectral Image Classification: A Review. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (11), pp.6547 - 6565. 〈10.1109/TGRS.2017.2729882〉. 〈hal-01687770〉

A Regression-Based High-Pass Modulation Pansharpening Approach

Gemine Vivone, Rocco Restaino, Jocelyn Chanussot. A Regression-Based High-Pass Modulation Pansharpening Approach. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, pp.1 - 13. 〈10.1109/TGRS.2017.2757508〉. 〈hal-01687064〉

Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification

Le Gan, Junshi Xia, Peijun Du, Jocelyn Chanussot. Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2017, pp.1 - 13. 〈10.1109/JSTARS.2017.2757475〉. 〈hal-01687085〉

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal